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IEEE DOI
1508
BibRef
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Springer DOI
1408
Accuracy
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He, K.M.[Kai-Ming],
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Deep Residual Learning for Image Recognition,
CVPR16(770-778)
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1612
Award, CVPR.
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IEEE DOI
1804
computational complexity, feedforward neural nets,
learning (artificial intelligence), neural net architecture,
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Convolutional neural network, Cross-layer interaction,
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IEEE DOI
2003
3D shape analysis, second-order pooling, spectral transform,
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Wang, S.D.[Shi-Dong],
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Multi-Granularity Canonical Appearance Pooling for Remote Sensing
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IEEE DOI
2004
Granular feature representation, transformation invariant,
Gaussian Covariance matrix, remote sensing scene classification
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2006
Feature pooling layer, Convolutional neural network,
Deep learning, Object recognition
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Elsevier DOI
2006
Deep learning, Image classification, Object detection,
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Ensemble Learning Approaches Based on Covariance Pooling of CNN
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Zhu, X.G.[Xiao-Guang],
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Graph-based reasoning attention pooling with curriculum design for
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IVC(115), 2021, pp. 104289.
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2110
Content-based image retrieval, Graph convolutional networks, Curriculum design
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Hssayni, E.H.,
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A Novel Pooling Method for Regularization of Deep Neural networks,
ISCV20(1-6)
IEEE DOI
2011
convolutional neural nets, image classification,
learning (artificial intelligence), pattern classification,
Spectral Dropout.
BibRef
Douillard, A.[Arthur],
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Podnet: Pooled Outputs Distillation for Small-tasks Incremental
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ECCV20(XX:86-102).
Springer DOI
2011
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Chen, Z.,
Zhang, J.,
Ding, R.,
Marculescu, D.,
ViP: Virtual Pooling for Accelerating CNN-based Image Classification
and Object Detection,
WACV20(1169-1178)
IEEE DOI
2006
Convolution, Task analysis, Object detection, Interpolation,
Acceleration, Redundancy, Computational modeling
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Kobayashi, T.[Takumi],
Global Feature Guided Local Pooling,
ICCV19(3364-3373)
IEEE DOI
2004
convolutional neural nets, feature extraction,
image classification, learning (artificial intelligence),
Probabilistic logic
BibRef
Wan, W.,
Chen, J.,
Li, T.,
Huang, Y.,
Tian, J.,
Yu, C.,
Xue, Y.,
Information Entropy Based Feature Pooling for Convolutional Neural
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ICCV19(3404-3413)
IEEE DOI
2004
convolutional neural nets, entropy, feature extraction,
image classification, image segmentation,
Training
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Gao, Z.T.[Zi-Teng],
Wang, L.M.[Li-Min],
Wu, G.S.[Gang-Shan],
LIP: Local Importance-Based Pooling,
ICCV19(3354-3363)
IEEE DOI
2004
convolutional neural nets, image classification,
importance sampling, learning (artificial intelligence),
Neural networks
BibRef
Huang, J.,
Li, Z.,
Li, N.,
Liu, S.,
Li, G.,
AttPool: Towards Hierarchical Feature Representation in Graph
Convolutional Networks via Attention Mechanism,
ICCV19(6479-6488)
IEEE DOI
2004
convolutional neural nets, feature extraction, graph theory,
learning (artificial intelligence), pattern classification,
Adaptation models
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Gao, Z.[Zilin],
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Global Second-Order Pooling Convolutional Networks,
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IEEE DOI
2002
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Xu, Y.,
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DCT Based Information-Preserving Pooling for Deep Neural Networks,
ICIP19(894-898)
IEEE DOI
1910
Deep neural network, information preservation, spectral pooling, 2D-DCT
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Hu, G.,
Dixit, C.,
Luong, D.,
Gao, Q.,
Cheng, L.,
Salience Guided Pooling in Deep Convolutional Networks,
ICIP19(360-364)
IEEE DOI
1910
Pooling, Salience feature, Classification, Edge
BibRef
Saeedan, F.[Faraz],
Weber, N.[Nicolas],
Goesele, M.[Michael],
Roth, S.[Stefan],
Detail-Preserving Pooling in Deep Networks,
CVPR18(9108-9116)
IEEE DOI
1812
Standards, Visualization, Convolutional neural networks,
Task analysis, Feature extraction, Distortion, Adaptive systems
BibRef
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Multiple Wavelet Pooling for CNNs,
WiCV-E18(IV:671-675).
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1905
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Yang, M.H.[Ming-Hsuan],
Lim, J.W.[Jong-Woo],
DFT-based Transformation Invariant Pooling Layer for Visual
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ECCV18(XIV: 89-104).
Springer DOI
1810
DFT magnitude pooling replaces the traditional max/average pooling
layer between the convolution and fully-connected layers.
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Yu, K.C.[Kai-Cheng],
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Statistically-Motivated Second-Order Pooling,
ECCV18(VII: 621-637).
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1810
Second-order network.
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Simon, M.[Marcel],
Gao, Y.[Yang],
Darrell, T.J.[Trevor J.],
Denzler, J.[Joachim],
Rodner, E.[Erik],
Generalized Orderless Pooling Performs Implicit Salient Matching,
ICCV17(4970-4979)
IEEE DOI
1802
CNN. Learn the pooling strategy also.
feature extraction, feedforward neural nets,
image classification, image matching, image representation,
Visualization
BibRef
Zhai, S.F.[Shuang-Fei],
Wu, H.[Hui],
Kumar, A.[Abhishek],
Cheng, Y.[Yu],
Lu, Y.X.[Yong-Xi],
Zhang, Z.F.[Zhong-Fei],
Feris, R.[Rogerio],
S3Pool: Pooling with Stochastic Spatial Sampling,
CVPR17(4003-4011)
IEEE DOI
1711
Convolution, Distortion, Feature extraction, Neural networks,
Standards, Stochastic, processes
BibRef
Cui, Y.,
Zhou, F.,
Wang, J.,
Liu, X.,
Lin, Y.,
Belongie, S.J.[Serge J.],
Kernel Pooling for Convolutional Neural Networks,
CVPR17(3049-3058)
IEEE DOI
1711
Kernel, Neural networks, Taylor series, Tensile stress, Training, Visualization
BibRef
Li, P.H.[Pei-Hua],
Xie, J.T.[Jiang-Tao],
Wang, Q.L.[Qi-Long],
Gao, Z.L.[Zi-Lin],
Towards Faster Training of Global Covariance Pooling Networks by
Iterative Matrix Square Root Normalization,
CVPR18(947-955)
IEEE DOI
1812
Covariance matrices, Graphics processing units, Training,
Matrix decomposition, Backpropagation, Symmetric matrices,
Computer architecture
BibRef
Chen, Z.Q.[Zi-Qian],
Lin, J.[Jie],
Chandrasekhar, V.[Vijay],
Duan, L.Y.[Ling-Yu],
Gated Square-Root Pooling for Image Instance Retrieval,
ICIP18(1982-1986)
IEEE DOI
1809
Logic gates, Principal component analysis, Training,
Benchmark testing, Image retrieval, Task analysis,
Learning to gate
BibRef
Xie, L.X.[Ling-Xi],
Tian, Q.[Qi],
Flynn, J.[John],
Wang, J.D.[Jing-Dong],
Yuille, A.L.[Alan L.],
Geometric Neural Phrase Pooling:
Modeling the Spatial Co-Occurrence of Neurons,
ECCV16(I: 645-661).
Springer DOI
1611
BibRef
Bell, S.[Sean],
Zitnick, C.L.[C. Lawrence],
Bala, K.[Kavita],
Girshick, R.[Ross],
Inside-Outside Net: Detecting Objects in Context with Skip Pooling
and Recurrent Neural Networks,
CVPR16(2874-2883)
IEEE DOI
1612
BibRef
Yang, F.,
Choi, W.,
Lin, Y.,
Exploit All the Layers: Fast and Accurate CNN Object Detector with
Scale Dependent Pooling and Cascaded Rejection Classifiers,
CVPR16(2129-2137)
IEEE DOI
1612
BibRef
Mopuri, K.R.[Konda Reddy],
Babu, R.V.[R. Venkatesh],
Object level deep feature pooling for compact image representation,
DeepLearn15(62-70)
IEEE DOI
1510
Computational modeling
BibRef
Yang, M.[Mu],
Li, B.[Brian],
Fan, H.Q.[Hao-Qiang],
Jiang, Y.N.[Yu-Ning],
Randomized spatial pooling in deep convolutional networks for scene
recognition,
ICIP15(402-406)
IEEE DOI
1512
deep convolutional networks
BibRef
Yoo, D.G.[Dong-Geun],
Park, S.G.[Sung-Gyun],
Lee, J.Y.[Joon-Young],
Kweon, I.S.[In So],
Multi-scale pyramid pooling for deep convolutional representation,
DeepLearn15(71-80)
IEEE DOI
1510
Accuracy
BibRef
Liu, L.Q.[Ling-Qiao],
Shen, C.H.[Chun-Hua],
van den Hengel, A.J.[Anton J.],
The treasure beneath convolutional layers:
Cross-convolutional-layer pooling for image classification,
CVPR15(4749-4757)
IEEE DOI
1510
DCNN.
BibRef
Gong, Y.C.[Yun-Chao],
Wang, L.W.[Li-Wei],
Guo, R.Q.[Rui-Qi],
Lazebnik, S.[Svetlana],
Multi-scale Orderless Pooling of Deep Convolutional Activation Features,
ECCV14(VII: 392-407).
Springer DOI
1408
Deep convolutional neural networks
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Efficient Implementations Convolutional Neural Networks .